NativeDatabase.connect opens a lazy SQLx writer pool from an Endpoint that
Python resolves from the final DATABASE_URL, read the way Prisma reads it:
Unix socket host, sslmode / sslaccept / sslcert with Prisma's verification
rules (prefer + strict asks the server once whether it offers TLS),
sslidentity / sslpassword handed over as PEM, schema, options, pgbouncer mode,
pool_timeout (0 waits forever) and socket_timeout. Options SQLx cannot send
are logged instead of failing boot
ConnectionBudget splits each worker's connection_limit between Prisma and the
native pool, so the budget an operator sized never grows. The Rust share is the
hard-coded RUST_RESERVED_CONNECTIONS and no setting names Rust, SQLx or Prisma.
It is 0 until a ported query needs the pool, so Prisma's URL is exactly what it
was, no native pool opens and nothing connects through SQLx. A connection_limit
that is not a positive integer is never split and goes to Prisma as before.
Once the share is above 0 Prisma keeps at least one connection, and pools are
tagged litellm-prisma and litellm-native in pg_stat_activity. Python keeps
minting RDS IAM and Entra tokens and hands each one to the pool
statement_cache_size sets the native statement cache (Prisma's default 100) and
pgbouncer=true turns it off. sqlx-postgres 0.9.0 still names every statement when
the cache is off, so Statements runs queries unnamed in that case, like Prisma,
until a release carries transact-rs/sqlx#4337. An application_name the operator
set is used on both pools
The SQL query files are pinned to LF so Windows checkouts hash them like .sqlx,
and the crate lists .sqlx in its package include so the core sdist builds offline
crates/db/AGENTS.md tracks parity with the Python Prisma stack
The bridge now links litellm-db, so the editable install compiles its queries.
The Rust DB workflow builds that install from the checked-in .sqlx metadata,
since it runs before the migrations that the online check needs
* ci: stop PR runs from writing Actions caches and remove the sccache GitHub cache backend
PR runs were saving their own cache copies and sccache was making one cache
request per compiled file, about 700 to 2000 per Rust build. Together they
pushed the repository past GitHub's cache storage and request limits, so
restores failed with 429 or found nothing and jobs rebuilt from scratch. The
rust-bridge job that gates every unit shard went from 0% cache misses on
Oct 21 to about 65% on Nov 11.
Caches are now saved only from main and restored everywhere else. The Rust DB
cache gets the same save-if guard the other Rust jobs already have. The lint
python and ui-api-types jobs restore the uv cache that the unit workflow saves
on main, because lint only runs on pull requests and could never save a cache
that another pull request can read. The Terraform provider jobs turn off
setup-go's built-in cache, which saves from every ref, and use a new
cache-go-build action that follows the existing restore-everywhere and
save-on-main pattern.
sccache is removed from the unit, Rust and Rust DB workflows. The Cargo target
directory caches they already restore stay in place.
The smoke diagnostics upload no longer fails a job whose checks passed.
* ci(smoke): install the prebuilt Rust bridge and cache wheels built from source
Every smoke job compiled the Rust bridge inside uv sync, about 4.5 minutes each
in 8 jobs per run, and the Python 3.14 and 3.15 jobs also compiled grpcio-tools,
numpy and jiter from source on every run because no wheels exist for those
versions yet. Smoke only ran on pull requests, so nothing it built could be
saved for the next run.
The Linux smoke jobs now restore the bridge that the unit workflow already
builds once and saves on main. The bridge is abi3, so the same build serves
every Python version. When the restore misses, which happens when a pull
request changes a Rust build input, the job restores the Cargo build cache and
compiles as before. The cache key and restore step move into a
restore-rust-bridge action that the unit workflow's producer job also uses, so
the key is defined once.
Smoke now also runs on pushes to main and prunes the uv cache down to wheels
built from source before setup-uv saves it. Pull requests restore that cache,
so 3.14 and 3.15 stop recompiling the same packages.
The windows-sdk job drops its Cargo cache step. Nothing ever saved a Windows
copy, and saving one from main would add about 2 GB to a cache that is already
near its limit.
* ci(integration): install the Rust gateway and fail forwarded_proxy when Rust is not in front
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci(integration): build the Rust gateway only for the management suites that run forwarded_proxy
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
pytest --ignore is a no-op for a file the shell glob already expanded, so the 25
files proxy-infra-root listed under --ignore still ran there as well as in their
own shards (805 duplicate tests, 18% of the shard's time). Switch them to
--deselect, which pytest applies to explicit arguments, and teach
assert_ci_coverage.py to read --deselect and [..] character-class globs.
Split the rest by first letter into proxy-infra-root-a-p and proxy-infra-root-q-z,
and give the three files that spawn a child python (test_component_allowlists,
test_prometheus_metrics_server, test_proxy_cli) their own 2-worker
proxy-subprocess shard so the children stop competing with 4 xdist workers on
a 4 vCPU runner.
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(proxy): supervise the opt-in Rust forwarding server
With LITELLM_RUST on, `litellm` and `python -m gateway.launch` start a
supervisor that runs the Rust gateway on the public port and Python on a
private loopback listener. Python still serves every route and sees each
request as if the client had connected to it directly.
Rust sends the client's TCP peer in `x-litellm-rust-peer`. The supervised
server turns server-level proxy headers off, and a middleware restores
the peer and then applies the operator's own trust, so client IPs,
trusted proxies and every IP-based setting behave the same with Rust on
or off.
The supervisor is transparent to Python. Signals pass through, Python's
exit status is the result, and both children shut down when the
supervisor's end of their stdin closes, so nothing outlives it. Rust
starts only once Python listens. When forwarding cannot start (no
gateway binary, TLS, a Unix socket, Hypercorn or Granian), the entry
point warns and serves from Python alone.
Every forwarding decision lives in litellm/proxy/rust_forward, paired
with the gateway-forward crate. OS differences sit behind a Platform
protocol, with Windows supported for local CLI runs. AGENTS.md files in
the touched folders state the boundary. The `--rust_gateway` flag is
gone in favor of the environment variable.
* refactor(proxy): switch Rust forwarding on with LITELLM_RUST_FIRST
Forwarding gets its own switch, independent of LITELLM_RUST, which keeps
selecting only the Rust paths inside the SDK. Neither turns the other on.
---------
Co-authored-by: narmstrong-berri <narmstrong@berri.ai>
helm/litellm becomes the only chart. Every container runs the one shared image with args only, monolith.enabled renders a single proxy Deployment, the retired chart's KEDA, extraResources, generated master key, helm test, minReadySeconds and rollout strategy settings are ported, and the bundled PostgreSQL and Redis subcharts are removed
Resolves LIT-8880
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: speed up image scans with an opt-in Rust profile
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix: separate cached Rust wheels by maturin profile
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(search): add Microsoft Web IQ provider
* fix(search): require HTTPS for Web IQ credentials
* fix(search): include Web IQ in the packaged endpoint catalog
* fix(search): register Microsoft Web IQ query pricing
* test(search): fix Web IQ exception assertion lint
* fix(search): type Web IQ response passthrough and search error headers
The type-check gate failed on 8 new unknown-type errors in the Web IQ
transformation. Build passthrough fields from a typed helper and validate
the response models directly, and type the base search error headers as
dict[str, str] so subclasses no longer inherit an unknown parameter.
* fix(search): raise Web IQ parse errors without widening the base header type
Typing the base search error headers as dict[str, str] made the shared
HTTP handler report one more argument-type error. Restore the base
signature and raise the same BaseLLMException directly from Web IQ.
---------
Co-authored-by: Gilad Katz <29776916+giladk1221@users.noreply.github.com>
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
* feat(router): add Switchyard Stage Router strategy
* feat(router): decision-model classifier and safer fallbacks for the Switchyard stage router
Moves to nemo-switchyard 0.4.0a1, which adds decision-model classifiers and makes
ModelCall.respond and fail async. The classifier is now a tagged union of an LLM
judge and a decision model, and decision calls go through the router's adecisions
so any decisions provider works. Algorithm misbehavior and library errors route to
the capable model instead of failing the request, and the session id plus client
session headers are always passed so capable holds apply without a classifier
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(router): keep the Switchyard router inside the type-check gate
The gate's environment did not install the optional switchyard extra, so every
libsy symbol counted as Unknown. nemo-switchyard joins proxy-dev like the other
optional runtime packages, and imports go through switchyard_rust.libsy, which
ships py.typed. Step handling moves into helpers so no Final is rebound in the
loop, and the dashboard API types are regenerated for the new routing fields
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(router): route Switchyard on every endpoint and reject unsafe router saves
The router now passes each request's call type to routing strategies, and
Switchyard converts the request to chat messages with the existing converter for
that call type before translating it. /v1/messages turns with tool or thinking
blocks used to fall back to the capable model, and now route on their signals.
The Anthropic converter can keep tool_result.is_error on an analysis copy, so
Switchyard sees tool failures there, while requests sent to models never carry it
Saves of a strategy router that lists itself now fail with a 400 instead of a
500 with a dead row, the capable and efficient models must differ, judge and
decision calls are not retried before failing open, the Switchyard routing
fields are classified for redaction, and the edit dialog returns the saved
router instead of the stale one
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(router): catch self-routing on patch and rename, and trim Switchyard tests
The self-routing check now reads the stored params through the decrypting
helper and runs when a patch carries no litellm_params, so a PATCH that points
a router at itself or renames it onto one of its targets returns 400 instead of
a 500 with a dead row. The Switchyard tests share one transcript builder and
router fixture, and reformatting noise in unrelated test files is reverted
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* refactor(ui): make Switchyard a strategy in the existing auto router form
Switchyard is now a card in the existing strategy picker. Picking it swaps the
tier and classifier fields for the Switchyard fields, and the shared name, team
and save button create it through one submit branch, the same flow as the other
strategies. The separate add form, the extracted picker and the hidden-form
swap are gone, and the edit dialog keeps its own Switchyard form
The judge's session_affinity and message_hash_fallback settings are removed,
since Switchyard rejects both for the stage router
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(ui): accept partial tool semantics and show server errors on model edit
The Switchyard tool semantics field rejected mappings that list only some
categories, although the server accepts them. Model PATCH errors were
reduced to a generic message, so it now goes through the shared API client
like model create and surfaces the server text
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* feat(ui): show the routing strategy picker up front in the auto router form
Every strategy was hidden behind an Advanced configuration link, so the
quick setup read as the only option and never said it creates a heuristic
router. The picker now always shows with Heuristics as the labelled
default, the quick setup sits under it, and picking another strategy opens
that strategy prefilled with the chosen models
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(ui): show the server's reason when a model settings or API key update fails
Both screens replaced the rejection with a fixed message, so a 400 such as
a router routing to itself read only as a generic failure
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* feat(router): route Switchyard on the Rust router core
main replaced the Python router core with the Rust one, which left
Switchyard routers registered nowhere. Register them with the other
pre-routing strategies, hand each strategy the endpoint the request came
through, and evict Switchyard routers on reload and delete like the rest
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(router): scope Switchyard sessions per API key and fix review gaps in the Switchyard form
Switchyard session state is keyed by a hash of the caller's key hash and session id, and client
session headers no longer reach Switchyard unscoped. The add form carries the quick setup models
into Switchyard, sends the team and access groups, clears a stale tool semantics error, and the
schema reads API-saved configs with nulls and structured decision instructions
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* test(router): make the Switchyard test folder a package
* fix(router): read the Switchyard session key hash only from the proxy's own metadata
* fix(proxy): keep nested nulls as null when masking deployment params
* fix(router): send Switchyard judge calls with the caller's own metadata bucket so tag routing applies
* fix(router): put Switchyard judge metadata in the bucket the judge endpoint reads
---------
Co-authored-by: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Reworks the redis-client, connect, resolve and testing crates, adds python-compat json loads and
TTL seconds, replaces the redis-store memory tier with a DualCache plus a shared failure log, and
gives the Python side the Redis timeout log throttle and a request OTEL parent span for service events
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
* feat(packaging): ship litellm-proxy wheels with the proxy extra
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(packaging): keep litellm-proxy out of the proxy extra until PyPI has it
The current release pipeline publishes litellm with whatever pyproject.toml lists, so pinning an unpublished package would break pip install litellm[proxy]. Images and CI still install the locally built gateway wheel
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
* feat(interactions): continue requires_continuation long decodes with continuation_token hops
A foreground Interactions request stops at Google's per-hop output cap with
status requires_continuation and a continuation_token. litellm now re-posts the
request with previous_interaction_id and continuation_token until the status
moves on, merging the hops into one interaction (last id, every hop's steps,
summed usage with initial_input_tokens from hop 1), for non-streaming and
streaming calls. continuation_token is accepted as a request param, and
requires_continuation is a known status for billing.
* refactor(gemini interactions): validate streaming chunks with model_validate
* fix(interactions): keep the held completion across stream hops, stop on a repeated continuation_token, follow the spec redirect
* fix(interactions): add grounding_tool_count per tool type, stop on any seen continuation_token, keep streaming past a [DONE] frame when a hop is pending
* ci(interactions): point the spec canary at aistudio.google.com, where Google moved the Interactions OpenAPI spec
* test(interactions): scripted Google ends each long decode hop with a [DONE] frame
* refactor(interactions): pass requires_continuation and continuation_token through instead of looping hops in the proxy
The caller drives the continuation, the same as against Google directly. /interactions accepts continuation_token in place of input so a resume request needs no input.
* test(proxy): assert the accepted /interactions bodies pass the required-param check
* fix(proxy): keep the router default fallback for input on /interactions, continuation_token stands in for it
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Adds the litellm-router-core crate, its step-driven PyO3 bridge
(RouterCoreHandle / RouterCall) and RustCore (litellm/router_rust/),
served behind LITELLM_RUST through the ROUTER route rule. The host
call-out is the only boundary: every attempt goes through Host::invoke,
there is no native executor path, and RustCore never builds, wraps or
forwards to PythonCore, so router_python_core.py stays deletable.
Attempt preparation lives in litellm/router_attempt.py so it outlives
router_python_core.py.
RustCore matches PythonCore's behavior, including its known bugs (fixed
in follow-up PRs). Routing decisions (strategy picks, routing groups,
PTU, pre-routing, pre-call checks, fallback walk, health routing, access
groups, weights, cooldowns, allowed fails, usage) live in the crate;
work that needs live Python objects (request shaping, endpoints,
streaming, model info facts, callbacks, alerting, prompt management)
lives in the host-side litellm/router_rust/ package (steps/, invoke/,
surface/) driven by Rust steps.
The crate owns the registry. It runs the deployment build loop (secret
references, ids, organization fan-out, install order) and asks the host
for what needs Python, one deployment at a time so errors surface where
Python raised them: secrets, validate, provider and added steps. It
holds the model list, the index maps and the pattern routers with the
Python core's history rules (host_index.rs), and answers the registry
lookups (lookup.rs). The Python views of that state in
litellm/router_rust/surface/ (model_list, index(), names(), patterns())
read the handle, and every write to them becomes a changeset. Model
info, deployment model info, configured limits and model group info are
merged in the crate (model_info.rs, group_info.rs) from catalog rows and
facts the host answers on a model_info step asked at query time, so
price reloads and register_model stay visible. The usage cache key
format is defined once, in the crate.
The crate records each attempt's outcome itself rather than through
litellm callbacks. A streamed attempt is deferred: it counts its
request when the stream opens, and the host's stream wrappers report
the success with the final usage, or a failure after the first chunk,
when the stream ends, as the Python core's callbacks do.
tests/unit/test_router/core characterizes PythonCore's public behavior
and runs every test on both cores, with nothing pending; only the names
of the Python core's own outcome callbacks are asserted on Python alone.
litellm.Router is now final: subclassing it raises TypeError at the class statement, and the
machinery that kept subclass overrides working is gone. The Rust core never honoured subclass
overrides, so they would have silently stopped running there. Custom routing goes through
register_plugin(), settings through apply() and outcomes through observe(). This is a breaking
change for anyone subclassing Router.
Fuzz parity fixes on top of the rebase: the Rust core keeps num_retries,
max_fallbacks, allowed_fails, retry_after, timeout, the tag settings,
routing_strategy_args and the fallback lists as given (the crate reads
them as numbers, update_settings casts as Python does); a deprecated
`model_list = [...]` write reaches the Rust core; a failed set_model_list
is all or nothing on both cores (the deployments served before the call
are put back and the error raised); config_deployments() is a generator and `del` of a plain
attribute works on the Rust core. Least-busy pick reservations from
main now work on the Rust core: its selectors share one reservation
store, routed calls reserve and free their unstarted picks, and
select() and batch cancel do not reserve. On the Python core a dropped
upsert takes its name back off deployment_names, and
get_deployment_order reads every numeric spelling as one int so mixed
orders no longer raise TypeError.
Rulings on the remaining differences, the Python core changed where it
was the odd one out: sync picks and picks by deployment id apply the
team filter as async picks do, so another team's deployment is refused;
the fallback lists and default_litellm_params in RouterSettings are
copies, so an in-place edit is ignored and settings change only through
apply(); one health record with an unreadable timestamp drops out alone;
an empty fallback entry is skipped; a bool cooldown_time is refused at
construction, a bool or string cooldown_time on a deployment or a call
falls back to the router default, and a failure status that no HTTP
status can be is recorded as 500; an infinite or NaN cost and a whole
number wider than 64 bits (ptu_shares) are refused on both cores with
the same error (litellm/router_utils/deployment_numbers.py); and a
/v1/messages stream whose provider iterator raises cools the deployment
and retries like a non-stream call, with the Rust core waiting for that
cooldown before its retry picks.
Differences the proxy parity fuzz and its regression tests found on the
Rust core: a file upload or a batch on an uploaded file falls back only
within its own model group, as on the Python core, since the file lives
with one provider (the host sends same_group_fallbacks_only and the walk
skips targets in other groups); Decisions calls drop deployments whose
provider has no Decisions config, and deployments that refuse the audio
or videos a call carries, unless that would leave none, as the Python
core does, while the retry count still reads the whole group; a file
upload that fails on one order level is created on the next level's
deployments rather than the first level again; and a response that
cannot be converted (a text_completion body without a message) reports
only its own traceback, so a rate limit handled by the attempt before it
no longer turns the error into a 429, and both cores return 500.
* ci(unit): fetch the merge base by sha for the db-and-spend schema check instead of cloning full history
* ci(unit): read the schema check arguments through one typed mapping
* ci(unit): give the schema check its own step with the ui-unit base fallback and register it with the merge-base step test
* ci(unit): keep the setup ceiling at 35 minutes, the install step never took over 33 seconds
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(decisions): add EmpirioLabs AI as a decisions provider
empiriolabs/aplomb-1 now works with litellm.decisions and litellm.systemone, the proxy /v1/decisions and /v1/systemone routes, the decision model guardrail and the Admin UI. Requests go to POST /v1/decisions on the EmpirioLabs AI API with the System One body, keyed by EMPIRIOLABS_API_KEY with an optional EMPIRIOLABS_API_BASE
Chat models keep routing through the existing OpenAI-compatible JSON provider, and a test pins that
* ci: run the EmpirioLabs AI decisions tests in the llms unit shard
* fix: align EmpirioLabs registration with CI gates
---------
Co-authored-by: Adam Dalloul <47503782+Adam-Dalloul@users.noreply.github.com>
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
PRs into rc/* branches never ran the OSV dependency scan because the
pull_request filter only listed main and litellm_**. rc branches only
take changes through PRs, so adding rc/** covers every change that
lands on them
* feat(realtime): support latest OpenAI audio models
* fix(transcription): type SDK stream duration metadata explicitly
* fix(realtime): preserve transport settings and translation duration
* refactor(realtime): reuse SDK client initialization
* fix(audio): bill finalized streams and authorize transcription models
* refactor(audio): keep transcription pricing selection together
* fix(realtime): reconcile model metadata and test contracts
* fix(realtime): preserve provider duration and model capabilities
* fix(realtime): keep transcription validation compatible with cost map guard
* fix(realtime): address transcription and billing review findings
* fix(realtime): preserve input usage and provider routing
* fix(realtime): bill retained output and route Azure aliases
* fix(realtime): reject malformed provider durations
* fix(realtime): preserve batch cache rates and Azure v1 routes
* fix(transcription): reject nonfinite provider durations
* refactor(realtime): align annotations with current lint rules
* fix(realtime): preserve translation deployment pricing and input usage
* test(transcription): verify synchronous stream finalization
* fix(realtime): retain missing translation input usage at close
* fix(transcription): reject streams that bypass output guardrails
* fix(realtime): reconcile audio protocols billing and current CI
* fix(realtime): trust confirmed formats and authorize default models
* fix(realtime): preserve absent models and reject unsupported guardrails
* fix(realtime): reject translation policy pipelines
* chore(ci): rerun transient infrastructure timing failure
* fix(realtime): reconcile audio protocols with current upstream
* fix(audio): validate provider responses at SDK boundaries
* fix(realtime): preserve minimum OpenAI SDK compatibility
* fix(realtime): preserve deferred Live session authorization
* fix(realtime): preserve upstream authentication and transcription behavior
* fix(audio): scope provider duration precedence to configured models
* fix(typing): avoid Final declarations inside transcription cost loop
* refactor(audio): reuse transcription mapping and duration resolution
* fix(realtime): enforce model budgets and preserve translation routing
* fix(audio): log streamed transcripts whose done event reports detected languages
* fix(audio): pass stream, languages and keywords through unchecked for non-OpenAI transcription providers
Providers without a streaming transcription handler got UnsupportedParamsError
for stream=True, languages and keywords, where the merge base forwarded them as
provider-specific params. Only openai and azure validate them now.
* fix(audio): bill streamed gpt-transcribe from the provider's reported duration
The stream path computed cost after logging had replaced the transcription's
duration usage, so it fell back to the measured file length while the
unstreamed call billed the provider's seconds.
* fix(transcription): log streamed transcription usage from the provider response
The stream logging path built the spend payload from the response after
its duration usage was transformed away, so whisper-1 with stream=true and
streamed gpt-transcribe logged an empty usage object. Bill and log from the
provider's TranscriptionResponse in both the sync and async paths.
* fix(realtime): keep main's routing, protocol and transport behavior around the new translation routes
- Send OpenAI realtime and translation client_secrets and calls through litellm's httpx client again, so ssl_verify and custom CA settings apply and upstream errors keep main's shape
- Use the input transcription model as the session model only for transcription sessions
- Drop the new /openai/v1 translation aliases and record each lazy feature's own routes, so the /openai pass-through and the MCP routes resolve as on main
- Let a configured Azure realtime_protocol win for every model, with GA as the default only when none is set
- Return x-litellm headers on streamed transcriptions and mark the request done
- Refuse translation sessions before connecting upstream when realtime guardrails apply or the provider is not OpenAI or Azure
- Accept json and text for gpt-transcribe, and drop other formats under drop_params instead of failing
- Bill translation sessions on output audio seconds only, at the single published rate
- Document the 201 SDP answer on the translation calls routes and the transcription 200 response
* fix(realtime): restore main behavior for guardrails, Azure transcription, and realtime clients
Streamed transcription runs unstreamed when a deployment post_call guardrail applies, so the
guardrail still sees the transcript. Azure transcription maps v1, latest and preview to the
default dated version on the deployments route for every model. The SDK client secret keeps
session.model ahead of model, realtime WebSockets connect to api_base again, realtime metadata
keeps only guardrails, and managed agents need an explicit transcription model. Reverts the two
unrelated streamed Responses error codes and the duplicate cached_tokens_details field, and the
gpt-transcribe format check only reads the request provider's cost map entry.
* fix(realtime): keep main's transcription, client secret and route behavior where the PR changed it
Transcription reaches every provider as on main again, sends stream only when asked, forwards language and
languages together, keeps form key order, bills provider seconds only when above zero, honors base_model for the
new flags, keeps main's Azure api_version on the proxy path, and streams carry chat's headers, an error event on a
mid-stream failure, and a spend row when the stream ends early. Translation cost reads both per-second rates from
one source and spend rows carry the billed seconds.
Realtime transcription WebSockets keep model in the upstream URL outside OpenAI's hosts, client secrets forward
explicit nulls, accept conversation and delay, and answer 400 on a malformed body, the /openai translation paths
are back to main, translation refuses only for guardrails realtime sessions run, nested aliases resolve and later
session.update models are authorized, translation client secrets run per-model auth, beta clients bridged to GA
get beta-shaped session events, and transcription sessions send the deployment model in both shapes.
* fix(realtime): address review findings on transcription billing, streaming and realtime auth
Per-second pricing replaces token billing only when the priced entry sets no token rates, translation rates fall back per key to the cost map, transcription cache keys include languages and keywords, a stream request a post_call guardrail forces unstreamed comes back as redacted SSE, Azure beta clients are bridged only for GA-only deployments, nested transcription models on the realtime WebSocket are checked against the key, and translation spend carries key and team metadata
* fix(azure): pick the GA realtime protocol for deployments whose model_info.base_model is GA-only
* fix(realtime): preserve prior merge resolutions after rebase
* fix(realtime): enforce translation policies and normalize Azure transcription URLs
* fix(cookbook): stop translation once output idles after the upload ends
* fix(realtime): bill translation on input audio and drop pre-read stream cost headers
Translation spend now uses the input audio seconds the session sent, priced
through input_cost_per_second, instead of the translated output duration.
A streamed transcription no longer returns cost headers before the stream is
read, since the cost is only known after the last event. Also removes an
unused realtime guardrail helper.
* fix(realtime): drop top-level session nulls, bill dropped transcription streams, and bridge beta response fields
Client secret, translation and transcription session bodies drop top-level nulls again, as main did, while nested nulls such as turn_detection still pass through. Transcription client secrets send the deployment model as session.model. A streamed transcription the client drops still runs its success callbacks, and the stream ends with data: [DONE] like OpenAI's. The beta to GA bridge maps max_response_output_tokens, speed and noise reduction, remaps response.create, and drops temperature under drop_params. The Entra token for realtime HTTP routes is fetched off the event loop
* test(e2e): cover realtime translation, live transcription, client secrets, and streamed transcription
* fix(realtime): close review gaps in translation billing, transcription streams, and Azure GA
Bill translation sessions from the deployment's own per-second rates, add per-second prices for
Azure gpt-4o transcribe models, keep stream-scoped guardrails seeing the caller's stream flag, map
mid-stream transcription errors to LiteLLM exceptions with a [DONE] frame, map GA responses back to
the beta shape for bridged beta clients, honor the minting key's guardrail opt-out on translation
calls, restore the Azure transcription session URL, and flag GA-only Azure realtime models
* fix(realtime): bill a translation rate the deployment omits from the session model
* test(e2e): check translated speech is not English instead of exact French wording
* fix(realtime): keep main's behavior for non-bridged backends, stream error frames, gateway URLs and Azure transcribe prices
Remap beta events to GA only for bridged beta clients and translation sessions, bridge beta clients on GA-pinned Azure deployments, log error-only upstream sessions as failures, flag dated Azure GA realtime models, drop the unpublished Azure per-second transcribe rates, log a mid-stream transcription failure once, keep main's SSE error frame outside transcription, drop unsupported transcription params under drop_params, keep the caller's Azure auth headers, keep a gateway /v1 path, let key opt-outs release string-callback guardrails on translation, and keep the cookbook waiting while a long upload is still sending
* fix(realtime): keep the proxy's realtime session gate off translation sessions
A key that opts out of a legacy callback guardrail now gets a working translation session, since the transcription-guardrail gate no longer sends a realtime session.update the translation upstream rejects
* fix(realtime): keep websocket model limits, bill measured transcription length, route transcription sessions on the allowed model
A WebSocket opened without a query model now authorizes as an empty model, transcription cost prefers the measured upload length over the provider's reported seconds, transcription sessions overwrite nested models with the authorized top-level model again, and a non-boolean transcription stream value answers 400 or is dropped under drop_params.
* fix(ci): copy the rust bridge extension into the openapi docs gate's base worktree
* fix(model-catalog): accept the realtime translation endpoints in the cost map
* fix(cookbook): close the translation session and read until session.closed
* fix(realtime): treat an empty transcription stream as unset and pass the cost-map guard
An empty `stream` form value runs the transcription unstreamed again, as
OpenAI does, instead of answering 400. The four translate cost-map entries
drop the translation routes from supported_endpoints until the main
branch's catalog parser accepts them, and the nested transcription model
lookup uses the router registry entry point.
* test(proxy): drop the mid-stream error frame pin that main's error shape change replaced
* fix(transcription): bill uploaded seconds when a stream fails mid-transcript
* fix(proxy): bill uploaded seconds when a streamed transcription breaks mid-stream
* fix(audio): keep transcription's positional arguments and redact streamed error details
- Move the new languages, keywords and stream options to the end of transcription's parameters, so a prompt or format passed by position keeps its meaning
- Redact internal hostnames, paths and tracebacks from a streamed transcription's mid-stream error event, as chat streams already do
- Move the CA-bundle check for the realtime HTTP routes, which serves real TLS on loopback, from the unit suite to the SDK integration suite
* fix(audio): stream the guarded transcript to SDK callers that asked for a stream
* fix(logging): keep a streamed transcript out of callbacks that turned off message logging
A streamed transcription's assembled response reached success callbacks with its text intact under turn_off_message_logging or a callback's message_logging=False, because the shared streaming redactor had no transcription branch. It now redacts the text, as the unstreamed call already does
* fix(proxy): stream a transcription the deployment streamed when the client sent no stream field
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(e2e): share the CLI version rule, env lockdown and rate limiter in tests/e2e/cli_harness
Moves the npm release rule (now taking the package name), the env allowlist plus fresh HOME, and the per-upstream rate limiter out of claude_code so the Codex suite can reuse them. Claude Code behavior is unchanged, its CI, cron and Dockerfile paths now call the shared resolver with @anthropic-ai/claude-code
* test(e2e): freeze the packument map and note the rate limiter self-test
* test(e2e): race real processes on one rate limiter bucket so a missing lock fails
Eight spawned processes start together and drain a bucket that never refills. With the flock they get exactly its burst between them, without it every attempt is granted
* test(e2e): keep the old Claude Code version resolver path for the Buildkite runner images
The runner Dockerfiles still run tests/e2e/claude_code/pr_gate_version_resolver.py and silently install @latest when it is missing, which drops the three day review window. The old path now forwards to cli_harness/npm_version_resolver.py
* refactor(proxy): inject database repositories through per-repository dependencies
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* test(proxy): model Prisma client cleanup
* test(rust): include NativeDatabase in bridge API surface
---------
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(usage): show subscription-covered providers and account fees separately
* fix(usage): write only the named fee columns and sync the root schema
The fee update now writes just the columns the request names, so two admins changing
different fields at once both land, and the dashboard edit form sends only what changed.
The root schema.prisma carries the new table like the two copies, and a billing period
anchored in the calendar's last month ends on December 31, 9999 instead of failing.
* fix(usage): register the subscription table span and keep provider logic under llms
* fix(subscription_accounts): register the fee routes as management routes and reject an empty account id
* fix(usage): run the new tests in CI and close subscription card review gaps
Adds the subscription test file to its unit shard, sets the migration lock timeout, rejects infinite fees, keeps the card from rewriting login files, lists each deployment once without aliases, shows the provider badge on entity views, and asks before removing a fee
* fix(usage): resolve named-credential ChatGPT accounts and keep admin viewers read-only on the fee card
* fix(usage): count a subscription fee only for accounts behind a current deployment
Usage listed a fee row for an account no deployment uses while another ChatGPT
deployment existed and dropped it when none did. It now lists only accounts behind a
current deployment in both cases. Adds a real proxy and Postgres integration test for
the fee API and for ChatGPT Responses rows on a deployment's own login, and names the
card's multi-component test with the integration suffix.
* fix(usage): keep a stored subscription fee listed after its account's deployments are removed
The usage route lists every account behind a subscription deployment plus every account carrying a stored fee, and reads the fees on every proxy, so a fee stays visible, counted, and editable for past ranges after its last deployment goes. The integration test now polls every day the calls spanned, so a UTC midnight crossing no longer fails it.
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Neither chart bundles Lens anymore. The Lens subchart, its lock entry,
the vendored chart archive, the templates/lens resources and the
bundled-Lens install smoke are gone. A connection-only lens block wires
the gateway and backend to a Lens deployed from its own chart, and an
enabled lensWorker value fails the render instead of being ignored.
Supersedes #47869, which made the same change for helm/litellm only.
* feat(providers): add CLF AI Gateway (clf_ai_gateway), OpenAI-compatible, 9 models
OpenAI-compatible gateway at https://api.clfaigateway.dev/v1 serving open-weight models (GLM, Kimi, DeepSeek, Qwen) on Cloudflare Workers AI upstream. Follows the DeepInfra pattern: ClfAiGatewayConfig(OpenAIGPTConfig), CLF_AI_GATEWAY_API_KEY / _API_BASE, provider registered in enum, provider lists, endpoint inference, lazy-import registry, ProviderConfigManager, validate_environment. Supported params deliberately omit functions/function_call/logit_bias (gateway returns 400 for them). 9 cost-map entries (root + backup) with measured context/output limits and currently billed prices; endpoint-support entry; README row. Mocked tests only. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(clf_ai_gateway): declare each model's real reasoning_effort levels
The per-level supports_*_reasoning_effort flags cannot express the sets
this gateway actually takes, so all nine models advertised a wrong one:
every model offered "minimal" and eight offered "none", neither of which
the gateway accepts, five dropped "xhigh", which it does accept, and
qwen3.8-27b offered "high", which it rejects. That set surfaces through
/model_group/info and the model picker, so a user picking a level off it
gets a 400 from the gateway.
Declare reasoning_effort_levels instead, taken from the gateway's own
GET /v1/public/models, which resolve_supported_reasoning_efforts reads
first and uses whole. Declaring it also stops clf_ai_gateway/deepseek-v4-pro
and clf_ai_gateway/deepseek-v4-flash inheriting effort metadata from the
unprefixed deepseek entries of the same name.
* test(clf_ai_gateway): name the gateway-rejected params instead of commenting them
* fix(clf_ai_gateway): list the provider in the proxy Add Model form
main now requires every LlmProviders value to have an entry in
provider_create_fields.json (or be frozen as unlisted), so the Add Model
dropdown cannot silently miss a backend provider. Add the clf_ai_gateway
entry in the same shape as the SCX.ai registration: required API key,
optional API base with https://api.clfaigateway.dev/v1 as placeholder, and
a cost-map model as the default model placeholder. A provider test pins
the entry.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* fix(clf_ai_gateway): sync prices and reasoning_effort levels with the live catalog
CLF AI Gateway raised its launch discount from 40% to 60% off list on
2026-09-26. It also re-measured every model's reasoning_effort levels
against the current Workers AI behaviour.
- input/output/cached per-token prices for all 9 models now match
GET https://api.clfaigateway.dev/v1/public/models to the nano; qwen3.8-27b
cached input also follows the upstream cached rate published on 2026-09-25
- reasoning_effort_levels: add "max" to deepseek-v4-flash, deepseek-v4-pro,
glm-5.2 and glm-5.3-flash; add "none" to kimi-k2.6 (it really disables
thinking there); drop "none" from glm-5.3 (upstream runs it as "max")
Context windows, output limits and vision flags are unchanged and were
checked against the same endpoint.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(clf_ai_gateway): drop a rebind-ok that no longer suppresses anything
After rebasing onto main, the type-discipline gate's new LIT013 rule flags
the `# rebind-ok` on the endpoint-inference `dynamic_api_key` assignment.
The rebind rule no longer fires on that line, so the suppression is inert.
The sibling endpoint branches carry no comment there either.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* test(clf_ai_gateway): expect the re-measured reasoning_effort levels
Follows the catalog sync: max on deepseek-v4-flash, deepseek-v4-pro,
glm-5.2 and glm-5.3-flash, none on kimi-k2.6, and no none on glm-5.3.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* test(clf_ai_gateway): move the provider tests to tests/unit/llms
main moved every per-provider test from tests/test_litellm/llms/<provider>/
to tests/unit/llms/<provider>/ (DeepInfra lives at
tests/unit/llms/deepinfra/). This branch still added the old path, so no CI
shard claimed it. That failed assert-ci-coverage and assert-shard-coverage,
and the file never ran (hence the low codecov/patch).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* test(clf_ai_gateway): assert routing, billing, and dropped params instead of pinned gateway facts
* fix(clf_ai_gateway): keep the type-discipline gate at its merge-base count
* chore(clf_ai_gateway): regenerate provider endpoint support
* ci(clf_ai_gateway): run the provider tests in the llms unit shard
* fix(clf_ai_gateway): drop the retired max_tokens field from the cost map rows
* fix(clf_ai_gateway): forward params the gateway accepts, drop undeclared reasoning levels, add deepseek-v4.1-flash
* fix(clf_ai_gateway): drop the unreachable non-chat supported-params branch
* fix(clf_ai_gateway): send a dict reasoning_effort as its level
* fix(clf_ai_gateway): refuse a reasoning_effort dict without a level unless drop_params drops it
---------
Co-authored-by: bap1106 <bap1106@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
* fix(ci): measure PR gates from the target branch's current tip
The lint gate base, the ui lint and ui unit test scopes, and the spend logs
guard compared the event's pull_request.base.sha with the head to find the
merge base. GitHub can leave base.sha weeks behind the base branch, and when
that old commit is an ancestor of a head that merged newer main, compare
returns the old commit itself, so the strict gates blamed main's own code on
the PR.
Compare base.ref with the head instead, passed through env, so GitHub
resolves the branch tip when the job runs. This is the same base make check
uses locally. The ui unit test push guard now keys on BASE_REF, which is
empty on push and workflow_dispatch.
The regression test runs every workflow step that reads merge_base_commit
against a fake gh backed by a temp repo with a stale base.sha, for main and a
litellm_/ base with a slash.
* fix(ci): check out the base branch's current guard code in the PR guards
* fix(ci): read the PR's current base branch so re-runs survive a deleted base
Every merge-base step now asks the pulls API for the PR's base branch instead of
reading the event's base.ref, which a re-run keeps after a stacked base is merged
and deleted. The guard checkouts go back to base.sha, which stays reachable.
* test(ci): mark the step script path Final
* fix(ci): keep the spend logs guard on main and run both guard scripts from the base branch tip
* fix(ci): measure from the event's base branch unless it was deleted, and test what each step hands on
A re-run after a retarget off main kept following the PR's current base, so lint, ui-unit and the cost map guard could pass without measuring against main. They now read the current base only when the event's base branch no longer exists. The tests run each merge base step whole and check the value its consumer gets
* fix(ci): follow a renamed base branch to its new name before the compare
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(proxy): stop answering with the literal string "None" on the proxy-server, auth, and health routes
The error objects these routes return defaulted `type` and `param` to the
four-character string "None", so a client's error handler matched no known
OpenAI type and fell into its generic branch.
Route them through the shared openai_error_payload helpers instead, so `type`
comes from the carried type or the HTTP status and `param` serializes as JSON
null.
* fix(proxy): stop answering with the literal string "None" on the spend and management routes
The error objects these routes return defaulted `type` and `param` to the
four-character string "None", so a client's error handler matched no known
OpenAI type and fell into its generic branch.
Route them through the shared openai_error_payload helpers instead, so `type`
comes from the carried type or the HTTP status and `param` serializes as JSON
null.
* test(proxy): pin the mid-stream failure frame and the DB outage body to a JSON null param
* fix(proxy): type upstream timeouts as timeout_error and update integration asserts for the null param
A 408 with no carried type now answers timeout_error instead of invalid_request_error, so a client branching on type keeps retrying it. Three integration suites still compared auth rejections against param="None".
* fix(proxy): keep a 408 on main's invalid_request_error type and pin the transcription file cap's 400
* fix(proxy): type a route failure by the status it answers with after the tag-routing and no-deployment remap
* fix(proxy): keep auth_error on proxy-raised 401/403 route failures and ban the string None as an error type or param
* fix(proxy): type a hook's 401 or 403 on completions, moderations and transcription by its status, as chat does
Only a status the proxy remapped into 401 or 403, such as a tag-routing denial, keeps auth_error.
* test(proxy): build the transcription file-cap test on a real router so it holds after the router refactor
* chore(caching)!: remove the disk cache backend
Drop the disk cache type and its diskcache dependency, along with the
caching install extra that existed only to pull it in. diskcache has not
had a release since August 2023
Cache now raises a ValueError listing the supported types when it gets a
type it does not know. Before, an unknown type built a Cache with no
backend, which only failed on first use
BREAKING CHANGE: cache_params type "disk" and the litellm[caching] extra
are gone. A proxy configured with type disk now refuses to start. Use
local or redis instead
* test(proxy): expect the unsupported cache type error from _init_cache
The test pinned the old fallthrough, where an unknown type left the cache
without a backend and the proxy hit an AttributeError reading it
* chore(deps): drop the diskcache scan ignore main extended
diskcache is no longer in the lock, so the ignore matches nothing
* fix(caching): ignore a leftover disk_cache_dir instead of crashing redis and s3
Dropping the disk_cache_dir parameter sent the key into **kwargs, which
the redis and s3 branches hand to their clients, so a config that moved
from the disk cache to redis or s3 and kept the key failed with a
TypeError. Accept the key again, ignore it, and log a warning
The unsupported type test now checks the whole supported list, and two
proxy docstrings no longer name disk as a cache backend
* fix(caching): warn about a leftover disk_cache_dir only once the cache is built
The warning ran before the type check, so a config with type disk logged
that the key was ignored and then failed on the type anyway. It now logs
only after a supported backend is built. The test also checks that a
cache without the key logs nothing
* test: add a manually dispatched integration trial on larger runners
Runs the providers, database and management integration groups on 16-core GitHub-hosted runners with Postgres and Redis as service containers, to measure wall time and how far each group scales with more workers
run_integration.sh now keeps a preset INTEGRATION_WORKERS, takes the service container names from the environment, and seeds test order from the GitHub run when there is no CircleCI one
* test: run the integration trial on push to this branch
* test: give integration trial workers a local redis-server
* test: run the providers trial on a 64-core runner with 32 workers
The 16-core run reached 53% of providers in 45 minutes with 16 workers, which points at CPU. Postgres gets max_connections=1000 and a 1 GB shm because one server now backs 32 worker proxies, where each CircleCI machine backs 4
* test: run every integration group in the trial on sized runners
* test: add a scan that refuses to upload integration results holding the license
* test: drop the member-name pass the archive byte scan already covers
* test: use a secret whose base64 and base64url encodings differ
* ci: run integration groups on run-ci pull requests and scheduled main runs
* ci: scope the integration license to a protected environment
* fix(proxy): default the uvicorn worker healthcheck timeout to 60 s so multi-worker boots survive a slow import
* fix(proxy): warn on uvicorn below 0.37 when multi-worker boots cannot get the longer healthcheck
uvicorn 0.33 to 0.36 has no timeout_worker_healthcheck, so the 60 s default never reaches it and a slow worker start still crash-loops with no hint. Print an upgrade warning when the proxy runs more than one uvicorn worker on such a version, keep granian out of the uvicorn-only path, and type the new tests.
* fix(proxy): name --run_granian in the healthcheck help text and add per-worker grace guidance to the helm chart
* fix(proxy): keep the litellm command light so spawned workers answer uvicorn's first health ping
Each worker uvicorn spawns re-runs the parent's main script before it starts
answering health pings. The litellm console script imported the whole SDK at
the top, so on a busy or CPU-limited host a new worker missed uvicorn's 5 s
ping and was killed in a loop. The script now runs litellm_cli.main:proxy_server,
which imports the proxy only when called, and gateway.launch imports LiteLLM
inside main the same way.
This replaces the earlier 60 s healthcheck default, so uvicorn's 5 s
healthcheck and SIGTERM timing stay as they are.
* fix(proxy): load the worker app before its log config and without GC pauses
* fix(gateway): ship litellm_cli in the gateway image
gateway.launch imports without_gc_pauses from litellm_cli, which the
gateway image never copied, so python -m gateway.launch failed at import.
Also build the gateway image when the launcher or litellm_cli changes.
* fix(proxy): apply the worker log config before the proxy app import, as earlier releases did
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(vector_stores): add Amazon OpenSearch as a vector store provider
* feat(ui): add Amazon OpenSearch to the vector store provider dropdown
* feat(vector_stores): add OpenSearch k-NN search as a vector store provider
* test(vector_stores): hold the OpenSearch provider tests alone for the first review
The provider source comes back in the next commits on this PR
* ci(tests): run the OpenSearch vector store unit tests
* fix(vector_stores): sign on.aws and FIPS OpenSearch hosts and reject missing AWS credentials before embedding
* fix(vector_stores): regenerate provider endpoint support for opensearch
* fix(vector_stores): keep opensearch retrieve, update, and delete off the index and refuse half-set AWS keys and per-account endpoints
* fix(vector_stores): return only scalar opensearch attributes and keep credential source output out of 401s
* test(vector_stores): inject a failing opensearch credential source instead of running a shell
* fix(vector_stores): sign opensearch for the endpoint's region, refuse list, add opensearch_index_name, honor drop_params
* fix(vector_stores): refuse a caller-chosen opensearch_index_name on search and rag query
* fix(vector_stores): refuse opensearch_index_name only on OpenSearch stores, keep the old 400 text
Other providers drop the key again as they did before OpenSearch support, and the four embedding keys keep their original error sentence.
* fix(vector_stores): refuse a caller index on slash-form OpenSearch stores
* ci: run the dashboard API types check on every change that can alter the OpenAPI spec
* chore: shorten the spec_pattern comment to a pointer at the CI filter
* chore: drop the spec_pattern comment
* docs: name the full ui-api-types trigger set in the make check header and dashboard AGENTS.md
* test: check make check and CI pick the same files for the API-type sync
* ci: run the Terraform provider endpoint audit on enterprise route changes
* test: keep the Terraform trigger test in the mapped workflow test file
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat: add BigModel provider with native Chat, Responses, and Messages APIs
Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com>
* test(providers): make BigModel unit tests a package
Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com>
* docs(providers): clarify BigModel parameter interface
Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com>
* fix(bigmodel): keep the Coding Plan base, surface Responses auth errors, and scope routes
Chat Completions keeps a GLM Coding Plan api_base. Responses raises the status from
BigModel's success=false body instead of returning an empty 200, and a JSON body
answering a stream request is replayed as a one-shot stream. Messages streams keep
ping and [DONE] lines. bigmodel leaves openai_compatible_providers so speech, image,
and transcription routes send it no request
* fix(responses): replay a JSON body on a stream request only when it has a Content-Length
A gateway that labels a chunked SSE stream as application/json kept streaming before
the replay, so only a sized body is read in full
* fix(bigmodel): scope the JSON stream replay to BigModel, map its business error codes, and echo requested tools
* fix(bigmodel): forward Codex's include and parallel_tool_calls, keep the OpenAI org header off BigModel, and pass unchanged Messages stream events through
* fix(bigmodel): map disabled thinking to low effort, keep Messages temperature, raise context window and content policy errors, and default parallel_tool_calls to true
* fix(bigmodel): keep the caller's reasoning_effort when Messages rewrites disabled thinking
* fix(bigmodel): keep context-window and content-policy errors on streamed Responses replays
---------
Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(cost_map): mark fireworks_ai/minimax-m3 as vision and pin capabilities verified against live calls
Fireworks' serverless minimax-m3 answers image inputs, but its model
listings say supports_image_input false, so the last two registry syncs
flipped supports_vision back to false and the Fireworks pre-flight turned
every image request into a 400 without ever calling the provider.
Flip both minimax-m3 rows back to supports_vision true and record the
live-call evidence in ci_cd/cost_map_pins.json. The cost map guard now
fails any PR that lowers a pinned capability, quoting the evidence, and
the sync bot may not touch the pins file.
* fix(cost_map): fail the guard when the pins file is missing
* fix(cost_map_guard): skip the pin checks on a branch cut before the pins file existed
* fix(ci): check cost map pins against the merge result and keep price sync PRs off the pins file
* fix(ci): run the cost map guard against the live base branch tip and re-run it when main's pins change
* fix(ci): pass the live base tip to the cost map guard through env and re-run every guard run on a pin change
main now runs the guard on pull_request with the base branch's copy of the script, so the workflow passes the
live tip in COST_MAP_GUARD_TARGET, which the older script ignores. The recheck reads past GraphQL's 100-file
cap, re-runs every guard run for a shared head, and fails when a re-run cannot start. The guard tells a merge
conflict apart from a failed merge-tree call.
* fix(ci): run the cost map pins recheck on litellm_ base branches too
* fix(ci): bar bot-opened PRs from the pins, recheck pin PRs on cost map changes, and skip the merge for untouched PRs
* fix(ci): read the pushed files from the compare API in the cost map pins recheck
The push event GitHub hands to Actions leaves each commit's added, modified and
removed lists null, so the recheck never saw a pins change and re-ran only the PRs
that touch the pins. Compare the push's before and after commits instead, and
re-check every cost map PR when the branch is new, the push was forced, the
compare fails, or it lists 300 files, the most it returns.
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
